Content Does Not Monetize Itself: Others in the Value Chain are Necessary

Businesses virtually never take positions that undermine or threaten their own interests. So how does Cloudflare benefit from its new emphasis on blocking language model scraping of web content?

By blocking unauthorized AI crawlers, Cloudflare helps its customers safeguard their original content from being used to train language models without consent or compensation. This is especially valuable for the publishers, media companies, and content-driven businesses that buy Cloudflare services. 

There also are new revenue opportunities. Cloudflare’s introduction of an AI scraping marketplace allows website owners to set terms and charge AI companies for access to their content. Cloudflare potentially earns fees or commissions, turning content protection into a business opportunity.

Cloudflare’s policies also provide more negotiating leverage for its content customers when negotiating licensing deals.

AI scraping can resemble denial-of-service attacks, overloading servers and degrading website performance. Cloudflare’s controls help prevent such problems.

The point is that the new policies are beneficial for Cloudflare, increasing its perceived value for its core clients; providing differentiation from competing alternatives and creating possible new revenue opportunities as well.

Lawsuits over copyright infringement and also technology solutions such as undertaken by Cloudflare to prevent web scraping are some content owner and content-supporting supplier responses to AI language model impact on their revenue models.

Still, content business models are going to have to change, as monetizable web traffic already is declining. Blocking of AI crawlers might slow down indexing activities, and raise the cost of doing so.

But content creators have no “right” to make money in the value chain. It always takes partners to monetize content or art. Language models might arguably pose an issue, just as controls on content scraping might be viewed as a help.

But content monetization always is dependent on others in the value chain.

Content Creators	Historical Value Chain Partners	Role in Monetization	Modern Value Chain Partners	Role in Monetization
Painters	Patrons (e.g., nobility, churches), art dealers, auction houses	Commissioned works, purchased paintings, or sold them to collectors; provided materials and exhibition spaces.	Galleries, online marketplaces (e.g., Saatchi Art), NFT platforms (e.g., OpenSea)	Display and sell artworks, authenticate pieces, or enable digital sales (e.g., NFTs); provide global exposure via online platforms.
Writers	Publishers, printers, booksellers	Printed and distributed books, serialized novels in magazines, paid advances or royalties.	Self-publishing platforms (e.g., Amazon KDP), literary agents, crowdfunding (e.g., Kickstarter)	Enable direct publishing and sales, connect writers to publishers, or fund projects via fan support.
Musicians	Record labels, concert promoters, sheet music publishers	Produced and distributed recordings, organized live performances, sold sheet music for home use.	Streaming platforms (e.g., Spotify, Apple Music), social media, Bandcamp	Distribute music globally, generate ad/subscription revenue, enable direct sales or fan-funded projects.
Sculptors	Patrons, city governments, workshops	Commissioned public or private sculptures, provided materials and studio space.	Art collectives, online galleries, 3D printing services	Facilitate sales through exhibitions, provide digital tools for creation, or connect with buyers online.
Actors	Theater companies, producers, patrons	Staged performances, paid actors for roles, attracted paying audiences.	Film/TV studios, streaming platforms (e.g., Netflix), talent agencies	Produce and distribute content, pay for performances, or monetize via subscriptions/ads; connect actors to opportunities.

Why 99.999 Percent Availability is Not Possible Anymore

Our user experience of applications, devices and networks is far from the “five nines” standards (99.999 percent availability) telcos used to tout. 

As a practical matter, today’s heterogenous, edge-powered, internet transport fabric, IP-based application environment absolutely means user experience cannot approach 99.999-percent availability for any applications. 

That might not apply to core systems in banking, financial trading or some security-critical use cases, but only to the core systems, not the end user access of those systems. 

The problem is that no matter what any single participant in the value chain might claim for its own availability, and even if that availability is between 99 percent and 99.99 percent, the entire end-to-end value chain depends on the sum total of availability across the whole value chain, and that math is challenging. 

Consider an example where contributor availabilities are:

• Device: 99%

• Home broadband access: 99.5%

• Internet backbone: 99.99%

• App server: 99.9%

• Local power: 99.5%

The end-to-end availability requires multiplying all those discrete availabilities. So the formula is 

0.99 × 0.995 × 0.9999 × 0.999 × 0.995 ≈ 97.4 percent. That means 229 hours of downtime per year, not the 5.26 minutes per year allowed by "five nines” standard.

The only reason end users seem unaware of the change is that much of the downtime happens when they are not actively using their connections (devices not present; devices in “do not disturb” mode; user is sleeping; apps not in immediate use). 

Value Chain Component	Typical Availability (%)	Major Downtime Factors
User Devices – Mobile	95%–99%	Battery loss, OS/software issues, dropped connections
User Devices – Fixed	96%–99.5%	Power outages, device crashes, local network (Wi-Fi) issues
Access Network – Mobile	97%–99.9%	Tower outages, congestion, interference, maintenance
Access Network – Fixed	98%–99.9%	Fiber/cable cuts, power issues, last-mile failures
Global Internet Backbone	99.99%+	Rare fiber cuts, DDoS attacks, routing errors
Application Servers (Cloud)	99.5%–99.99%	Cloud region outages, software bugs, maintenance, cyberattacks
Local Power Supply	99.0%–99.9% (urban)	Grid instability, storms, infrastructure failures
End-to-End Availability	Often < 95%–98%	Cumulative failures across components

Some Problems Just Cannot be Fixed: Lumen Technologies, for Example

Some problems are nearly impossible to fix. Consider Lumen Technologies, a mashup of the former Level 3 Communications capacity business and the former US West local telco business. Right now, Lumen is facing declining revenue growth and a substantial debt load, acquired at the same time the firm made acquisitions of capacity assets growing its share of that business. 

The current business strategy seems clear enough: refocus on what used to be the Level 3 Communications business (capacity and enterprise) and eventually get out of the former local telco business. 

To be sure, the former US West (then Qwest, then CenturyLink) always was structurally challenged. The former US West had low customer density and limited business customer potential. 

The former means its network costs on a per-location business were going to be high, while the latter means its organic growth potential is limited. All the other Regional Bell Operating Companies formed during the breakup of the AT&T system had denser network footprints and higher business customer potential. 

On top of that, US West never developed a facilities-based mobility business, as did the other firms that eventually became AT&T and Verizon. And since mobility now is the revenue driver for a local telco, that also has hampered Lumen’s growth. 

Today, the enterprise business represents about 75 percent of total Lumen revenues.

The issue is how Lumen could divest essentially all of its former local telco business, and to whom. Private equity is a possibility. Perhaps some rural telcos or independent broadband internet providers could buy parts of Lumen. 

Perhaps some assets could be sold to new public-private partnerships, cooperatives or other joint ventures. That would be especially true of the rural assets. 

By some estimates, the metro areas of Denver, Seattle, Phoenix, Salt Lake City, Portland, Minneapolis-St. Paul, and Orlando generate between 60 percent and 70 percent of all the local telco revenues Lumen generates. 

The point is that nobody has been able to overcome the density and customer upside issues US West, Qwest, CenturyLink, Lumen have faced since the beginning. Prior to 2000, US West tried to create a position in cable TV services. Under Qwest, from 2000 to 2010 or so, the company shifted to the capacity business. 

CenturyLink seemingly wanted both scale benefits and entry into a higher business customer profile. Lumen is reversing that focus, selling off essentially all the former CenturyLink rural telco assets and now its mass markets fiber to home business. 

Lumen will become Level 3 Communications. The legacy telco assets will eventually be divested, somehow, to various buyers, in some way. It seems unlikely the whole former US West business will be appealing to any single buyer; and perhaps no single buyer will have the capital and business plan requiring all the assets, in any case. 

Some problems seemingly cannot be fixed.

Water Footprint Matters for the "Great American Desert"

It is easy to forget that the U.S. Intermountain West; the states containing 40 million people who use the Colorado River; and the Great Plains states (the land east of the Sierra Nevada and west of the 96th meridian) are essentially deserts.

And that matters because water is life. 

By now most of us are familiar with the concept that every physical object and every intangible product has both a carbon footprint and a water footprint. Most people probably do not pay much attention to water footprint. Many of us in the U.S. intermountain west and Great Plains likely do pay attention, as befits people living in deserts. 

Product	Unit	Water Footprint (approx.)
Beef	1 kg	15,415 liters 415
Pork	1 kg	5,988 liters 1
Chicken	1 kg	4,325 liters 1
Cheese	1 kg	3,178 liters 1
Eggs	1 egg	52 gallons (197 liters) 6
Milk	1 liter	1,021 liters 1
Rice	1 kg	2,497 liters 1
Bread (wheat)	1 kg	1,608 liters 1
Tomatoes (fresh)	1 kg	214 liters 7,1
Apples	1 kg	822 liters 1
Almonds	1 kg	16,194 liters 1
Chocolate	1 kg	17,196 liters 1
Coffee	1 cup	34 gallons (129 liters) 6
Wine	1 glass	34 gallons (129 liters) 6
Jeans (cotton)	1 pair	2,108–2,866 gallons (8,000–10,850 liters) 8,6
T-shirt (cotton)	1 shirt	659 gallons (2,720 liters) 8,6
Smartphone	1 device	3,190 gallons (12,760 liters) 8,6
Car	1 car	13,737–21,926 gallons (52,000–83,000 liters) 8,6
Leather shoes	1 pair	2,113–3,626 gallons (8,000–13,730 liters) 8,6
Paper (A4 sheet)	1 piece	1.3 gallons (5.1 liters) 8

The point is that in many parts of the world, water footprint matters as much as carbon footprint.